LEMON : THE LEARNING EFFICIENCY COMPUTATION MODEL FOR ASSESSING LEARNER CONTEXT IN E-LEARNING

International Journal on Integrating Technology in Education (IJITE) Vol.2, No.3, September 2013
DOI :10.5121/ijite.2013.2303 23
LEMON : THE LEARNING EFFICIENCY
COMPUTATION MODEL FOR ASSESSING LEARNER
CONTEXT IN E-LEARNING
R.Sunitha1
and G.Aghila2
1
Department of Computer Science, Pondicherry University, Puducherry, India
2
Department of Computer Science, National Institute of Technology, Puducherry
ABSTRACT
Current E-learning systems are focusing on providing learning solutions depending upon the context of the
learner. Efforts have been put in the delivery of contents, learning path and support based on the learner
context. As the learner’s context serves as base for triggering adaptation of learning solutions, a clear
understanding and an accurate definition of learner context is necessary. Different perspectives of context
have been discussed in literature. In this paper a different perspective for defining learner context is
employed and a feature viz. Learning Efficiency that consolidates the learner context has been arrived. The
different elements that constitute Learning efficiency have been identified using which a computational
model of Learning Efficiency called LEMOn has been proposed in order to quantify the learner context.
The model has been subjected to statistical evaluation in order to check for its correctness and was found
to represent the learner context efficiently.
KEYWORDS
E-learning, Learner context, Learning Efficiency, LEMOn
1. INTRODUCTION
E-Learning refers to the use of any digital media and information and communication and web
based technologies for learning. E-learning facilitates learners with the liberty of choosing a
subject, the sequence of learning, the complexity of the learning content and the type of learning
content also. With the proliferous growth of Internet, e-learning has become a popular application
and number of developments has been made in the field of e-learning to provide better learning
experience to the learners. Major works concentrate on providing flexible solutions tailored for
each individual learners [1][2][3][4][5][6]. On the other end, works that focus on integrating the
best teaching theories and strategies in the e-learning context thereby enhancing the technology
through a strong theoretical base are also carried out[7][8][9][10]. The major focus of all these
efforts is to provide efficient learning solutions to the unguided learner to accomplish his learning
objective. In this work, the role of context awareness in e-learning has been discussed and a
solution for adopting context awareness effectively in e-learning has been presented.
Context awareness refers to reduction of explicit information a user needs to provide to
accomplish a computing task [11]. The understanding and use of context in e-learning helps in the
unobtrusive provision of suitable learning solutions that match the context of the learner.
Different parameters are employed in order to define the context of a learner [12]. The context
information can be used in order to adapt the presentation, content, learning path and support
solutions provided to the learners. The focus of this study is to identify the parameters that
comprise the learner’s context so that the resulting adaptation is efficient.

24
1.1 Motivations
The definition of context varies dependent on the application for which it is used for. As the
context information is used to facilitate the users to accomplish their objectives, a careful analysis
and selection of the attributes that constitute the context is essential for efficient adaptation of
services to the application users. Thus in e-learning, where the learners are characterised with
self-directed and self-paced learning, it is necessary to define what constitutes the context of a
learner. This requires a deep understanding of e-learning, its environment and the objective of the
learners. Though the study of literature reveals that different parameters are employed in order to
define the context of a learner, yet the appropriateness of those parameters in relation to the
accomplishment of the learner’s objectives is a question. Thus there is a need for a precise
definition for context in e-learning and the identification of appropriate parameters of context that
will help the learners to achieve their learning objectives to their satisfaction.
1.2 Contributions
In this work we employ a novel perception to the definition of context and use it to define
Learning Efficiency, a factor that consolidates the context of a learner. We have proposed the
mathematical formulation of Learning Efficiency as LEMOn- the Learning Efficiency
computation MOdel for consolidating the learner context in a quantified form. The proposed
model has been evaluated statistically for correctness and was found effective.
The paper has been organized as follows. Section two presents the study of concepts related to
general e-learning systems and context aware e-learning systems. The works in literature related
to the present study has been presented in Section three. The discussion of the LEMOn has been
presented in Section four. The experiments conducted to evaluate the proposed model, the results
obtained and the analysis of the results has been portrayed in Section five. Section six draws the
conclusion and future directions of this work.
2. BACKGROUND THEORIES
In this section we will discuss about the theoretical aspects of adaptation in E-learning and
Context awareness in E-learning.
2.1. Adaptive E-learning Systems
E-learning removes the need of traditional classroom face-to-face teaching and learning. As the
learners are of different characteristics, the process of learning is also quite different; hence the
one-content-fits-all approach of teaching is not desirable. Hence, researches on e-learning are
focusing on providing adaptive solutions suiting the need of the learner. An adaptive e-learning
system comprises of a user model, an adaptation model and an adaptation engine in order to
provide flexible learning solutions. The learner model serves as the basis for adaptation. The
adaptation model dictates the rules for adaptation based on the learner model and the adaptation
engine executes the rules when necessary.
The elements of the user model are the characteristics of the learner like demographic data,
learning styles, interests, preferences, goals, knowledge level, ability etc. The different aspects of
e-learning subjected to adaptation learning objects, contents of learning objects, learning
sequence, learning path, assessment and support.

25
2.2. Context Awareness in e-learning systems
Context-aware applications are those that detect, interpret aspects of the application user and
environment characteristics including the device capabilities and network conditions. They
dynamically adapt their behaviour based on those aspects that describe the context of the
application and the user. A context aware e-learning system provides adaptive solutions based on
the context of the learner unlike conventional adaptive e-learning systems that adapt on the basis
of the learner models. The context is determined in various ways. A pervasive e-learning
application determines the learner’s context through the location, time and device information. A
collaborative e-learning system determines the social context of the learner which includes his
peer learners, the collaborations he has with them etc. A psychological context based e-learning
system provides adaptive solutions based on psychological aspects of the learner like changing
motivational levels etc. An affective e-learning system provides adaptive solutions based on the
mood and emotions of learners.
Figure 1 illustrates the general architecture of a context aware e-learning system. The context
reasoner component acquires the information from the e-learning environment and from a stored
user model and reason out the present context of the learner and supplies the leaner’s context to
the adaptation engine. The adaptation engine component offers adapted solutions to the learner
based on the reasoned context using the rules provided by the adaptation model for the given
context. The objects drawn from the hyperspace are subjected to adaptation based on the
adaptation rules.
Figure 1 The General Architecture of a Context Aware E-learning System
3. RELATED WORKS
In this section we will be presenting the study and analysis of literature confined to works related
to learning efficiency computation and the e-learning systems whose behaviour is dependent on a
variety of context features.
3.1 Learning Efficiency
A seminal work on computation of learning efficiency is that of Fletcher [13] who defines
learning efficiency in perspectives of learner’s performance in the test and the learning efficiency
is given by
Context
Reasoner
SystemInterface
Adaptation
Engine
User
Model
Knowledge
&Hyper space
Domain
Model
Adaptation
Model

26
o
t
e
i
o
i
t
e 
Where i is the number of test items, o is the number of opportunity to learn items and t is the test
score. But in an ideal learning environment it is not only the performance matters for computing
the learning efficiency. There are other parameters that may also be contributing to compute the
learning efficiency of a learner.
3.2 Context Aware E-learning Systems
Study of literature reveals the different aspects have been concentrated in handling context in e-
learning applications. The different perspectives of context aware e-learning systems are the
selection of elements of context, context management involving acquisition, representation and
reasoning and application of context information for adaptation, adapting different elements of e-
learning based on specific context. With respect to the attributes, Study of literature reveals that
mainly four attributes are employed in defining the learner context viz. the physical elements,
temporal elements, resources and the user himself.
In [14], three levels of contextualization are considered viz. learning process level, organization
level and individual learner level. These multilevel contexts are utilized in their project ‘Learning
In Process’ to provide highly contextualized e-learning experience for the adult learners of
knowledge intensive organizations. The learner context includes previous knowledge, goals,
interactions and preferences. Organizational context is defined using organizational unit, role(s),
business process and task. The technical context is formulated using the characteristics of learners
platform (such as operating system, browser, plugins etc.), network bandwidth and available
audio.
In [15], a hierarchical schema defining a learning context has been proposed which includes the
physical context that describes the learning resources (attributes like resource identifier, category,
name, and capabilities) and a list of persons (attributes like identification, name, and role), digital
context (e-books, e-papers, simulators, and Web-based learning services), device context
(hardware, the software, and the network connectivity of the e-learning device) and learner
information context(name, expertise, or interests, learning status, team membership)
In [16] , attempt has been made to find the right collaborator, right information and right learning
services in the right place at the right time in order to provide context aware content access for
learners with portable devices .
In [17] the authors integrate content provision, learning process and learner personality in an
integrated semantic e-learning framework. A context model is proposed to structure the semantics
of contextual relations and concepts in various contexts, such as learning content description,
learning model, knowledge object representation and learner personality.
In [12] , in order to generate context aware learning contents, various levels of contextualization
are considered such as the profile context, infrastructure context, preference and learning context
of learner
In [18] multi-agent systems forms the basis of the context management. The Sharable Content
Object Reference Model (SCORM) standard and semantic Web ontology is used for learning
content storage, sequencing and adaptation.

27
In [19] the conceptual model of the learning situation known as the Learning Context Model
(LCM) is built using the technological, subject domain, pedagogical, psychological aspects to
implement adaptive, personalised learning environment.
In [20] the aspects of mobility of devices such as GPS (Global Positioning System) receivers,
motion sensors, etc. have been used to characterize the context of the learner in the development
of a middleware for m-learning applications.
In [21], a novel appraoch based on learning orientation theory has been proposed for a
personalized navigation strategy. The learning orientation is used to classify learners into four
categories and there by to provide a personalized learning path in order to improve the learning
efficiency.
An important characteristic of context is its dynamicity. Hence we need attributes that are
collected during the learning process that change dynamically which in trigger the context
switching to happen.
The next section focuses on defining context in perspectives of dynamism and the discussions
related to it.
4. PROPOSED MODEL
4.1 Defining Learner Context: The Learning Efficiency Measure
4.1.1 The learning context
Context is defined as any information that characterizes the situation of an entity. Context
information is incorporated in an application with an objective of adapting the service depending
on the change in context. The definition of context should be made by considering the objectives
of an application. In any kind of learning, the learning objective is to acquire new knowledge and
skills efficiently. Thus in e-learning, the learner’s context has to be defined precisely so as to
provide better adaptive learning solutions to fulfil this objective. In e-learning, the contributions
of device, environment, location and time data as described in literature does not act as true
factors that characterizes the situation of a learner. Factors beyond these really act as indicators of
a learner’s context.
In this work we are using the argument of [22] for defining the learning context. According to
[22], context is beyond those factors that describes a setting or situation; it is a feature of
interaction built upon these factors. The term context in e-learning should consolidate the current
learning situation and act as a trigger to improve or maintain the situation based on the condition
of the situation. Normally in e-learning context is seen as an external feature independent from
the activities of the learner [23]. Hence we need indicators that reflect the real learning context of
the student. An e-learning environment is filled with a set of learners of various levels and
capacities. The learners are provided with learning materials that differ in granularity, complexity
and type. The different interactions that a learner has with the e-learning environment are
accessing learning objects, accessing help items, taking tests, sharing with the peer learners etc.
The factors of the learning environment and the learner’s interaction with the environment
together define the context of the learner.
Using these elements of interaction and features of environment as indicators, we derive the
Learning Efficiency, as a factor that consolidates the context of a learner. The learning efficiency
dictates the degree of adaptation. An efficient learner is focused and needs less direction thereby

28
less adaptation, whereas an inefficient learner needs more directions and a high level of
adaptation to fulfil his learning objective. Thus learning efficiency represents the real learning
context of a learner.
4.1.2 The Learning efficiency measure
Efficiency in general is defined as maximizing the output with the given inputs. Normally the
learning efficiency is assessed mainly based on the performance of the learner in the tests [4]. But
the intrinsic features of E-learning inflict the definition of learning efficiency in a multifaceted
way encompassing the temporal, navigational and performance aspects of learning.
4.2 LEMOn – The Learning Efficiency Computation MOdel
Given a set of learning items I, a set of test elements T and set of help items H, the learning
efficiency is computed as follows. The aforementioned set of items comprehensively defines the
Opportunity to Learn [4] Item Set.
The Learning efficiency score is evaluated using three parameters viz. learner’s temporal aspect
of learning, learner’s performance, and the learner’s retention in learning .
4.2.1 Temporal aspect of learning efficiency:
The temporal aspect of learning efficiency defines how quick the learner is able to learn. This
factor is influenced directly by the learner’s knowledge level, his ability as well as the nature of
the learning material. If these factors are incompatible, then the learning time increases thereby
decreasing the learning efficiency. The temporal aspect of learning efficiency is defined as the
ratio of perseverance to the opportunity to learn and is given by
)1(
a
sa
t
t
tt
E


Where ts is the time spent on learning, the perseverance factor and ta is time available to learn, the
opportunity to learn factor.
4.2.2 Retention aspect of learning efficiency
The retention aspect of learning efficiency defines how well the learner is able to learn with the
provided learning material. This factor is greatly influenced by the nature of the material provided
to the learner. If the material matches with the needs of the learner then the retention score
increases else decreases. The retention aspect of learning efficiencyis given by
)2(
o
so
r
h
hh
E


Where hs is the number help items sought and ho is the total number of opportunity to learn help
items.
4.2.3 Performance aspect of learning efficiency
The performance aspect of learning efficiency defines how well the learner exhibits his
knowledge. This aspect is directly influenced by the learner’s knowledge level and his ability.
The better the learner’s knowledge level and his ability the better the performance score of the

29
learner. The performance aspect of learning efficiency is defined as the ratio of number of correct
response to the number of opportunity to learn test items. Eq. 3 gives the formulation of the
performance aspect of learning efficiency.
)3(
o
t
Ep 
Where o is the number test items drawn from the opportunity to learn test item set O and t is the
number of items answered correctly in o.
Thus the learning efficiency score is calculated by summing the individual scores of performance,
temporal and retention factors. The Learning Efficiency (LE) is given in Eq. 4.
)4(rtp EEELE 
The Learning Efficiency score calculated using this formula lies in the range 0 to 3. The higher
the score the large is the learning efficiency.
5. IMPLEMENTATION
5.1 Experiment
In order to evaluate the correctness of LEMOn, an experiment was conducted. The model
encompasses the temporal aspect of the learner, the retention aspect of the learner and the
performance aspect of the learner. Hence the experiments were conducted with the objective of
checking the relevance of these parameters in the proposed measure. Further the interdependence
between these parameters has also been experimented.
5.1.1 Experimental set up
The Java platform has been used to implement the proposed model. An opportunity to learn item
set was constructed with 1000 learning items comprising 50 modules. The learning items have
been drawn from the computer science domain. The learning items were of different file types,
complexity level and granularity. The opportunity to learn help item set was constructed as a
support to learners with an average of 10 items per module. The opportunity to learn test items
was constructed for each module. Learners were randomly selected with different capacities and
abilities and were allowed to take the modules and subsequently the tests attached with each of
the modules. In order to evaluate the results independent of the learners, the collected data was
grouped corresponding to the sessions for interpretation of the results.
5.2 Evaluation Metrics
The objective of the experiment is to identify the correctness of the model. The correctness of the
model can be evaluated by identifying the appropriateness of the parameters selected for defining
the model. Hence the model has to be evaluated statistically to find out the relevance of the
parameters.
Correlation refers to the statistical relationship involving dependence. The correlation analysis
has been done with the experimental results in order to find the degree of dependence between the
temporal, retention and the performance parameters as a whole with the learning efficiency. The
correlation between the temporal, retention and performance parameters individually has also
been identified through the experiments. The Karl Pearson’s Correlation coefficient is used in
evaluating the degree of dependence between the variables.

30
5.3 Results
The results of the experiment conducted at various time periods have been presented in Table 1.
Table 1. Results of the experiment.
Session
Id
Mean
Time
spent
(min)
Mean
help
items
accessed
Mean
Test
score
Mean
Efficiency
score
1 490 7 4.64 0.92
2 452 7 5.04 1.09
3 385 5 6.79 1.55
4 331 5 7.02 1.71
5 320 4 7.42 1.81
6 369 5 7.12 1.64
7 341 4 7.9 1.88
8 354 4 7.55 1.78
9 337 4 7.63 1.82
10 292 4 7.67 1.92
11 220 2 8.86 2.31
12 348 4 7.81 1.83
13 226 2 8.89 2.29
14 494 7 4.66 0.92
15 284 3 8.44 2.1
The correlation values between the different parameters and learning efficiency has been given in
Table 2.
Table 2. Results of the Correlation analysis
Time &
Learning
Efficiency
Retention
&
Learning
Efficiency
Performance
& Learning
Efficiency
Time &
Retention
Time &
Performance
Retention &
Performance
Correlation
Value
-0.98 -0.99 0.99 0.96 -0.95 -0.99
5.4 Results Analysis
The correlation analysis presented in Table 2 based on the data presented in Table 1indicate that
there is a strong correlation between the parameters and the proposed Learning Efficiency
measure. The correlation values are near to 1 indicating strong correlation.
The temporal aspect of learning has a negative correlation with Learning efficiency indicating
that the more time the learner spend on learning than the prescribed maximum time indicates
inefficient learning.
The Correlation analysis between retention and learning efficiency results in strong negative
correlation signifying that the more the learners sought to support point out their learning
inefficiency.

31
The performance aspect and learning efficiency has a positive correlation value of 0.99 indicating
that as performance increases the learning efficiency also increase.
The correlation values of these three aspects with the learning efficiency indicate that the learner
is finding it difficult to reach his learning objective with the given setup which results in poor
learning efficiency. Hence this is an indication to trigger adaptation.
Thus our argument of adopting learning efficiency as a consolidating feature of context holds
true.
The results of the correlation analysis between the individual parameters also substantiate this
view point.
Hence the proposed model has been proved to be correct and sufficient to stand as the context
feature of the learner to trigger an adaptation.
6. CONCLUSION AND FUTURE WORKS
Recent works in e-learning are focused on providing flexible learning solutions tailored to the
individual need of the learners. The needs of the learners are obtained by understanding the
learner and his context. In this work a novel definition of learner context has been followed. Base
on this definition a model of a measure viz. the Learning Efficiency measure has been proposed
that acts a consolidation of learner context. The proposed LEMOn model encompasses three
aspects of the learner i.e. the temporal, retention and performance aspects to define his context.
LEMOn has been evaluated statistically for its correctness and has been proved to be correct. The
completeness and soundness of the LEMOn can be evaluated by applying this model to various e-
learning scenarios in future.
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Authors
Ms. R.Sunitha has completed her MTech in computer Science And
Engineering from Pondicherry University and is currently doing her Ph. D.
Research in the same institution. Her areas of interest are E-learning,
Knowledge Management and Software engineering.
Dr. G.Aghila is a Professor at Department of Computer Science, National
Institute of Technology, Puducherry. She has got a total of 20 years of
teaching experience. She has graduated from Anna University, Chennai, India.
She has published nearly 50 research papers in reputed journals and
conferences. Her area of interest include Intelligent Information
Management, artificial intelligence, text mining and semantic web
technologies

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